Dietary Patterns during Pregnancy and Gestational Weight Gain: A Systematic Review.
Larissa Bueno FerreiraCecília Viana LoboAline Elizabeth da Silva MirandaBrenda da Cunha CarvalhoLuana Caroline Dos SantosPublished in: Revista brasileira de ginecologia e obstetricia : revista da Federacao Brasileira das Sociedades de Ginecologia e Obstetricia (2022)
The present systematic review (PROSPERO: CRD42020148630) hypothesizes the association of excessive weight gain during pregnancy with dietary patterns composed of ultraprocessed foods. Thus, the objective was to investigate the association between dietary patterns after analysis and weight gain during pregnancy. The search for articles was performed in nine databases. Two reviewers selected the articles in the databases and extracted from them the data used in the review. Two scales were used to evaluate the quality of the selected studies: New Castle-Ottawa Quality Assessment for cohort-based studies and Appraisal tool for Cross-Sectional Studies (AXIS) for cross-sectional-based studies. In total, 11 studies were identified with sample size variation ( n = 173-5,733). Women presenting more adherence to healthy and traditional patterns (fruits, vegetables, salads, nuts, and dairy) recorded less excessive gestational weight gain (GWG). Higher intake of mixed patterns and western patterns rich in ultraprocessed foods were associated with a higher prevalence of excessive GWG (24.48-55.20%). Gestational dietary patterns a posteriori - derived that have presented ultraprocessed components rich in fat and sugars presented association with high GWG; healthy and traditional dietary patterns were related to better mother-child health conditions, such as adequate GWG.
Keyphrases
- weight gain
- body mass index
- birth weight
- cross sectional
- systematic review
- case control
- weight loss
- physical activity
- risk factors
- type diabetes
- metabolic syndrome
- electronic health record
- case report
- risk assessment
- quality improvement
- pregnancy outcomes
- deep learning
- insulin resistance
- human health
- health risk
- artificial intelligence
- drug induced